OrderFlow [Adjustable] | FractalystWhat's the indicator's purpose and functionality?
This indicator is designed to assist traders in identifying real-time probabilities of buyside and sellside liquidity .
It allows for an adjustable pivot level , enabling traders to customize the level they want to use for their entries.
By doing so, traders can evaluate whether their chosen entry point would yield a positive expected value over a large sample size, optimizing their strategy for long-term profitability.
For advanced traders looking to enhance their analysis, the indicator supports the incorporation of up to 7 higher timeframe biases .
Additionally, the higher timeframe pivot level can be adjusted according to the trader's preferences,
Offering maximum adaptability to different strategies and needs, further helping to maximize positive EV.
EV=(P(Win)×R(Win))−(P(Loss)×R(Loss))
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What's the purpose of these levels? What are the underlying calculations?
1. Understanding Swing highs and Swing Lows
Swing High: A Swing High is formed when there is a high with 2 lower highs to the left and right.
Swing Low: A Swing Low is formed when there is a low with 2 higher lows to the left and right.
2. Understanding the purpose and the underlying calculations behind Buyside, Sellside and Pivot levels.
3. Identifying Discount and Premium Zones.
4. Importance of Risk-Reward in Premium and Discount Ranges
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How does the script calculate probabilities?
The script calculates the probability of each liquidity level individually. Here's the breakdown:
1. Upon the formation of a new range, the script waits for the price to reach and tap into pivot level level. Status: "⏸" - Inactive
2. Once pivot level is tapped into, the pivot status becomes activated and it waits for either liquidity side to be hit. Status: "▶" - Active
3. If the buyside liquidity is hit, the script adds to the count of successful buyside liquidity occurrences. Similarly, if the sellside is tapped, it records successful sellside liquidity occurrences.
4. Finally, the number of successful occurrences for each side is divided by the overall count individually to calculate the range probabilities.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
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What does the multi-timeframe functionality offer?
In the adjustable version of the orderflow indicator, you can incorporate up to 7 higher timeframe probabilities directly into the table.
This feature allows you to analyze the probabilities of buyside and sellside liquidity across multiple timeframes, without the need to manually switch between them.
By viewing these higher timeframe probabilities in one place, traders can spot larger market trends and refine their entries and exits with a better understanding of the overall market context.
This multi-timeframe functionality helps traders:
1. Simplify decision-making by offering a comprehensive view of multiple timeframes at once.
2. Identify confluence between timeframes, enhancing the confidence in trade setups.
3. Adapt strategies more effectively, as the higher timeframe pivot levels can be customized to meet individual preferences and goals.
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What are the multi-timeframe underlying calculations?
The script uses the same calculations (mentioned above) and uses security function to request the data such as price levels, bar time, probabilities and booleans from the user-input timeframe.
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How does the Indicator Identifies Positive Expected Values?
OrderFlow indicator instantly calculates whether a trade setup has the potential for positive expected value (EV) in the long run.
To determine a positive EV setup, the indicator uses the formula:
EV=(P(Win)×R(Win))−(P(Loss)×R(Loss))
where:
P(Win) is the probability of a winning trade.
R(Win) is the reward or return for a winning trade, determined by the current risk-to-reward ratio (RR).
P(Loss) is the probability of a losing trade.
R(Loss) is the loss incurred per losing trade, typically assumed to be -1.
By calculating these values based on historical data and the current trading setup, the indicator helps you understand whether your trade has a positive expected value over a large sample size.
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How can I know that the setup I'm going to trade with has a postive EV?
If the indicator detects that the adjusted pivot and buy/sell side probabilities have generated positive expected value (EV) in historical data, the risk-to-reward (RR) label within the range box will be colored blue and red .
If the setup does not produce positive EV, the RR label will appear gray.
This indicates that even the risk-to-reward ratio is greater than 1:1, the setup is not likely to yield a positive EV because, according to historical data, the number of losses outweighs the number of wins relative to the RR gain per winning trade.
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What is the confidence level in the indicator, and how is it determined?
The confidence level in the indicator reflects the reliability of the probabilities calculated based on historical data. It is determined by the sample size of the probabilities used in the calculations. A larger sample size generally increases the confidence level, indicating that the probabilities are more reliable and consistent with past performance.
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How does the confidence level affect the risk-to-reward (RR) label?
The confidence level (★) is visually represented alongside the probability label. A higher confidence level indicates that the probabilities used to determine the RR label are based on a larger and more reliable sample size.
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How can traders use the confidence level to make better trading decisions?
Traders can use the confidence level to gauge the reliability of the probabilities and expected value (EV) calculations provided by the indicator. A confidence level above 95% is considered statistically significant and indicates that the historical data supporting the probabilities is robust. This high confidence level suggests that the probabilities are reliable and that the indicator’s recommendations are more likely to be accurate.
In data science and statistics, a confidence level above 95% generally means that there is less than a 5% chance that the observed results are due to random variation. This threshold is widely accepted in research and industry as a marker of statistical significance. Studies such as those published in the Journal of Statistical Software and the American Statistical Association support this threshold, emphasizing that a confidence level above 95% provides a strong assurance of data reliability and validity.
Conversely, a confidence level below 95% indicates that the sample size may be insufficient and that the data might be less reliable . In such cases, traders should approach the indicator’s recommendations with caution and consider additional factors or further analysis before making trading decisions.
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How does the sample size affect the confidence level, and how does it relate to my TradingView plan?
The sample size for calculating the confidence level is directly influenced by the amount of historical data available on your charts. A larger sample size typically leads to more reliable probabilities and higher confidence levels.
Here’s how the TradingView plans affect your data access:
Essential Plan
The Essential Plan provides basic data access with a limited amount of historical data. This can lead to smaller sample sizes and lower confidence levels, which may weaken the robustness of your probability calculations. Suitable for casual traders who do not require extensive historical analysis.
Plus Plan
The Plus Plan offers more historical data than the Essential Plan, allowing for larger sample sizes and more accurate confidence levels. This enhancement improves the reliability of indicator calculations. This plan is ideal for more active traders looking to refine their strategies with better data.
Premium Plan
The Premium Plan grants access to extensive historical data, enabling the largest sample sizes and the highest confidence levels. This plan provides the most reliable data for accurate calculations, with up to 20,000 historical bars available for analysis. It is designed for serious traders who need comprehensive data for in-depth market analysis.
PRO+ Plans
The PRO+ Plans offer the most extensive historical data, allowing for the largest sample sizes and the highest confidence levels. These plans are tailored for professional traders who require advanced features and significant historical data to support their trading strategies effectively.
For many traders, the Premium Plan offers a good balance of affordability and sufficient sample size for accurate confidence levels.
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What is the HTF probability table and how does it work?
The HTF (Higher Time Frame) probability table is a feature that allows you to view buy and sellside probabilities and their status from timeframes higher than your current chart timeframe.
Here’s how it works:
Data Request : The table requests and retrieves data from user-defined higher timeframes (HTFs) that you select.
Probability Display: It displays the buy and sellside probabilities for each of these HTFs, providing insights into the likelihood of price movements based on higher timeframe data.
Detailed Tooltips: The table includes detailed tooltips for each timeframe, offering additional context and explanations to help you understand the data better.
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What do the different colors in the HTF probability table indicate?
The colors in the HTF probability table provide visual cues about the expected value (EV) of trading setups based on higher timeframe probabilities:
Blue: Suggests that entering a long position from the HTF user-defined pivot point, targeting buyside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Red: Indicates that entering a short position from the HTF user-defined pivot point, targeting sellside liquidity, is likely to result in a positive expected value (EV) based on historical data and sample size.
Gray: Shows that neither long nor short trades from the HTF user-defined pivot point are expected to generate positive EV, suggesting that trading these setups may not be favorable.
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How to use the indicator effectively?
For Amateur Traders:
Start Simple: Begin by focusing on one timeframe at a time with the pivot level set to the default (50%). This helps you understand the basic functionality of the indicator.
Entry and Exit Strategy: Focus on entering trades at the pivot level while targeting the higher probability side for take profit and the lower probability side for stop loss.
Use simulation or paper trading to practice this strategy.
Adjustments: Once you have a solid understanding of how the indicator works, you can start adjusting the pivot level to other values that suit your strategy.
Ensure that the RR labels are colored (blue or red) to indicate positive EV setups before executing trades.
For Advanced Traders:
1. Select Higher Timeframe Bias: Choose a higher timeframe (HTF) as your main bias. Start with the default pivot level and ensure the confidence level is above 95% to validate the probabilities.
2. Align Lower Timeframes: Switch between lower timeframes to identify which ones align with your predefined HTF bias. This helps in synchronizing your trading decisions across different timeframes.
3. Set Entries with Current Pivot Level: Use the current pivot level for trade entries. Ensure the HTF status label is active, indicating that the probabilities are valid and in play.
4. Target HTF Liquidity Level: Aim for liquidity levels that correspond to the higher timeframe, as these levels are likely to offer better trading opportunities.
5. Adjust Pivot Levels: As you gain experience, adjust the pivot levels to further optimize your strategy for high EV. Fine-tune these levels based on the aggregated data from multiple timeframes.
6. Practice on Paper Trading: Test your strategies through paper trading to eliminate discretion and refine your approach without financial risk.
7. Focus on Trade Management: Ultimately, effective trade management is crucial. Concentrate on managing your trades well to ensure long-term success. By aiming for setups that produce positive EV, you can position yourself similarly to how a casino operates.
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🎲 Becoming the House (Gaining Edge Over the Market):
In American roulette, the house has a 5.26% edge due to the 0 and 00. This means that while players have a 47.37% chance of winning on even-money bets, the true odds are 50%. The discrepancy between the true odds and the payout ensures that, statistically, the casino will win over time.
From the Trader's Perspective: In trading, you gain an edge by focusing on setups with positive expected value (EV). If you have a 55.48% chance of winning with a 1:1 risk-to-reward ratio, your setup has a higher probability of profitability than the losing side. By consistently targeting such setups and managing your trades effectively, you create a statistical advantage, similar to the casino’s edge.
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🎰 Applying the Concept to Trading:
Just as casinos rely on their mathematical edge, you can achieve long-term success in trading by focusing on setups with positive EV. By ensuring that your probabilities and risk-to-reward (RR) ratios are in your favor, you create an edge similar to that of the house.
And by systematically targeting trades with favorable probabilities and managing your trades effectively, you improve your chances of profitability over the long run. Which is going to help you “become the house” in your trading, leveraging statistical advantages to enhance your overall performance.
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What makes this indicator original?
Real-Time Probability Calculations: The indicator provides real-time calculations of buy and sell probabilities based on historical data, allowing traders to assess the likelihood of positive expected value (EV) setups instantly.
Adjustable Pivot Levels: It features an adjustable pivot level that traders can modify according to their preferences, enhancing the flexibility to align with different trading strategies.
Multi-Timeframe Integration: The indicator supports up to 7 higher timeframes, displaying their probabilities and biases in a single view, which helps traders make informed decisions without switching timeframes.
Confidence Levels: It includes confidence levels based on sample sizes, offering insights into the reliability of the probabilities. Traders can gauge the strength of the data before making trades.
Dynamic EV Labels: The indicator provides color-coded EV labels that change based on the validity of the setup. Blue indicates positive EV in a long bias, red indicates positive EV in a short bias and gray signals caution, making it easier for traders to identify high-quality setups.
HTF Probability Table: The HTF probability table displays buy and sell probabilities from user-defined higher timeframes, helping traders integrate broader market context into their decision-making process.
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Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Quantitative
Trend Forecasting - The Quant Science🌏 Trend Forecasting | ENG 🌏
This plug-in acts as a statistical filter, adding new information to your chart that will allow you to quickly verify the direction of a trend and the probability with which the price will be above or below the average in the future, helping you to uncover probable market inefficiencies.
🧠 Model calculation
The model calculates the arithmetic mean in relation to positive and negative events within the available sample for the selected time series. Where a positive event is defined as a closing price greater than the average, and a negative event as a closing price less than the average. Once all events have been calculated, the probabilities are extrapolated by relating each event.
Example
Positive event A: 70
Negative event B: 30
Total events: 100
Probabilities A: (100 / 70) x 100 = 70%
Probabilities B: (100 / 30) x 100 = 30%
Event A has a 70% probability of occurring compared to Event B which has a 30% probability.
🔍 Information Filter
The data on the graph show the future probabilities of prices being above average (default in green) and the probabilities of prices being below average (default in red).
The information that can be quickly retrieved from this indicator is:
1. Trend: Above-average prices together with a constant of data in green greater than 50% + 1 indicate that the observed historical series shows a bullish trend. The probability is correlated proportionally to the value of the data; the higher and increasing the expected value, the greater the observed bullish trend. On the other hand, a below-average price together with a red-coloured data constant show quantitative data regarding the presence of a bearish trend.
2. Future Probability: By analysing the data, it is possible to find the probability with which the price will be above or below the average in the future. In green are classified the probabilities that the price will be higher than the average, in red are classified the probabilities that the price will be lower than the average.
🔫 Operational Filter .
The indicator can be used operationally in the search for investment or trading opportunities given its ability to identify an inefficiency within the observed data sample.
⬆ Bullish forecast
For bullish trades, the inefficiency will appear as a historical series with a bullish trend, with high probability of a bullish trend in the future that is currently below the average.
⬇ Bearish forecast
For short trades, the inefficiency will appear as a historical series with a bearish trend, with a high probability of a bearish trend in the future that is currently above the average.
📚 Settings
Input: via the Input user interface, it is possible to adjust the periods (1 to 500) with which the average is to be calculated. By default the periods are set to 200, which means that the average is calculated by taking the last 200 periods.
Style: via the Style user interface it is possible to adjust the colour and switch a specific output on or off.
🇮🇹Previsione Della Tendenza Futura | ITA 🇮🇹
Questo plug-in funge da filtro statistico, aggiungendo nuove informazioni al tuo grafico che ti permetteranno di verificare rapidamente tendenza di un trend, probabilità con la quale il prezzo si troverà sopra o sotto la media in futuro aiutandoti a scovare probabili inefficienze di mercato.
🧠 Calcolo del modello
Il modello calcola la media aritmetica in relazione con gli eventi positivi e negativi all'intero del campione disponibile per la serie storica selezionata. Dove per evento positivo si intende un prezzo alla chiusura maggiore della media, mentre per evento negativo si intende un prezzo alla chiusura minore della media. Calcolata la totalità degli eventi le probabilità vengono estrapolate rapportando ciascun evento.
Esempio
Evento positivo A: 70
Evento negativo B: 30
Totale eventi : 100
Formula A: (100 / 70) x 100 = 70%
Formula B: (100 / 30) x 100 = 30%
Evento A ha una probabilità del 70% di realizzarsi rispetto all' Evento B che ha una probabilità pari al 30%.
🔍 Filtro informativo
I dati sul grafico mostrano le probabilità future che i prezzi siano sopra la media (di default in verde) e le probabilità che i prezzi siano sotto la media (di default in rosso).
Le informazioni che si possono rapidamente reperire da questo indicatore sono:
1. Trend: I prezzi sopra la media insieme ad una costante di dati in verde maggiori al 50% + 1 indicano che la serie storica osservata presenta un trend rialzista. La probabilità è correlata proporzionalmente al valore del dato; tanto più sarà alto e crescente il valore atteso e maggiore sarà la tendenza rialzista osservata. Viceversa, un prezzo sotto la media insieme ad una costante di dati classificati in colore rosso mostrano dati quantitativi riguardo la presenza di una tendenza ribassista.
2. Probabilità future: analizzando i dati è possibile reperire la probabilità con cui il prezzo si troverà sopra o sotto la media in futuro. In verde vengono classificate le probabilità che il prezzo sarà maggiore alla media, in rosso vengono classificate le probabilità che il prezzo sarà minore della media.
🔫 Filtro operativo
L' indicatore può essere utilizzato a livello operativo nella ricerca di opportunità di investimento o di trading vista la capacità di identificare un inefficienza all'interno del campione di dati osservato.
⬆ Previsione rialzista
Per operatività di tipo rialzista l'inefficienza apparirà come una serie storica a tendenza rialzista, con alte probabilità di tendenza rialzista in futuro che attualmente si trova al di sotto della media.
⬇ Previsione ribassista
Per operatività di tipo short l'inefficienza apparirà come una serie storica a tendenza ribassista, con alte probabilità di tendenza ribassista in futuro che si trova attualmente sopra la media.
📚 Impostazioni
Input: tramite l'interfaccia utente Input è possibile regolare i periodi (da 1 a 500) con cui calcolare la media. Di default i periodi sono impostati sul valore di 200, questo significa che la media viene calcolata prendendo gli ultimi 200 periodi.
Style: tramite l'interfaccia utente Style è possibile regolare il colore e attivare o disattivare un specifico output.
OptiRange | FractalystWhat’s the purpose of this indicator?
This indicator is designed to integrate probabilities with liquidity levels, while also providing a mechanical method for identifying market structure by using Fractals by Williams.
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How does this indicator identify market structure?
This script identifies breaks of market structure by analyzing candle closures above or below swing levels.
As soon as a candle has closed above or below the initial swing on your charts, the script validates that there is at least one swing preceding the break before confirming it as a structural break.
Once a break is occured then it assigns a numeric ID to the break starting from 1 and draws two extremities: one as liquidity and the other as invalidation (LIQ/INV).
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What do the extremities show us on the charts?
you'll see two clear extremities on your charts:
1. The first extremity represents the structural liquidity level. (LIQ)
2. The other extremity indicates the level that, if price breaks through it, results in a structural shift to the opposite side. (INV)
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How does it calculate probabilities?
Each break of market structure, denoted as X, is assigned a unique ID, starting from X1 for the first break, X2 for the second, and so on.
The probabilities are calculated based on breaks holding, meaning price closing through the liquidity level, rather than invalidation. This probability is then divided by the total count of similar numeric breaks.
For example, if 75 out of 100 bullish X1s become X2, then the probability of X1 becoming X2 on your charts will be displayed as 80% in the following format: ⬆ 75%
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What are the Fractal blocks?
Fractal blocks refer to the most extreme swing candle within the latest break. They can serve as significant levels for price rejection and may guide movements toward the next break, often in confluence with probability analysis for added confirmation.
If the price retraces back to a bullish fractal block, we aim to look for buy/long positions. Conversely, if the price retraces back to a bearish fractal block, we aim to look for sell/short positions.
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What are mitigations?
Mitigations refer to specific price action occurrences identified by the script:
1- When the price reaches the most recent fractal block and confirms a swing candle, the script automatically draws a line from the swing to the fractal block bar and labels it with a checkmark.
1- If the price wicks through the invalidation level and then retraces back to the fractal block while forming a swing candle, the script labels this as a double mitigation on the chart.
This level will serve as the next potential invalidation level if a break occurs in the same direction.
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What does the bottom table display?
The bottom table presents numeric breaks across multiple timeframes, with the text color indicating the trend direction. Enabling traders to assess the higher timeframes market trend without needing to switch between timeframes manually.
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How to use the indicator?
1. Add "OptiRange | Fractalyst" to your TradingView chart.
2. Choose the pair you want to analyze or trade.
3. Start with the 12-month timeframe.
4. Use the table bias with the maximal settings to find the lowest timeframe that’s showing you the mitigation (✓)
5. Confirm that the probability of the current liquidity is higher than 50%.
6. Place your limit order at the Fibonacci level of 0.618 of the mitigation candle.
7. Set your stop-loss at the mitigation level.
8. Determine your take profit based on the liquidity of the current timeframe, or if possible, the liquidity of a higher timeframe in the same direction; otherwise, use the liquidity of the current timeframe.
9. Risk adjustment and Trade management based on your personal preferences.
Example:
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User-input settings and customizations
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What makes this indicator original?
- This script leverages Fractals, a fundamental concept in many trading methodologies.
- For a break to be considered valid, price must have at least two swings:
a swing high followed by a swing low for bullish breaks and a swing low follow by a swing high for bearish breaks.
- This means that each swing point is confirmed by the formation of two candles on its left and two candles on its right, totaling 5 candles for each swing high and swing low, thus requiring 10 candles overall. (This strict rule ensures a thorough assessment of market structure before confirming a break.)
- The script assigns a unique numerical ID to each break of structure, starting from 1.
This numbering system enables the script to calculate the probability of the most recent break becoming the next break, while also factoring in the trend direction.
- Additionally, this script provides insights into higher timeframes' break IDs in the bottom/top centre table, keeping traders informed about the overall higher timeframe picture.
- By integrating these methodologies, the script introduces a unique and systematic method for identifying market structure, thereby enhancing its originality in guiding trading decisions.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data. By utilizing our charting tools, the buyer acknowledges that neither the seller nor the creator assumes responsibility for decisions made using the information provided. The buyer assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses. Therefore, by purchasing these charting tools, the customer acknowledges that neither the seller nor the creator is liable for any unfavorable outcomes resulting from the development, sale, or use of the products.
The buyer is responsible for canceling their subscription if they no longer wish to continue at the full retail price. Our policy does not include reimbursement, refunds, or chargebacks once the Terms and Conditions are accepted before purchase.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer.
Price Scenarios - The Quant ScienceGENERAL OVERVIEW
Price Scenarios - The Quant Science is a quantitative statistical indicator that provides a forecast probability about future prices moving using the mathematical-statistical formula of statistical probability and expected value.
HOW TO USE
The indicator displays arrow-shaped signals that represent the probable future price movement calculated by the indicator, including the current percentage probability. Additionally, the candlesticks are colored based on the predicted direction to facilitate visual analysis. By default, green is used for bullish movements and red for bearish movements. The trader can set the analysis period (default value is 200) and the percentage threshold of probability to consider (default value is greater than 0.50 or 50%) through the user interface.
USER INTERFACE
Lenght analysis: with this features you can handle the length of the dataset to be used for estimating statistical probabilities.
Expected value: with this feature you can handle the threshold of the expected value to filter, only probabilities greater than this threshold will be considered by the model. By default, it is set to 0.50, which is equivalent to 50%.
Design Settings: modify the colors of your indicator with just a few clicks by managing this function.
We recommend disabling 'Wick' and 'Border' from the settings panel for a smoother and more efficient user experience.
Kalman Filter by TenozenAnother useful indicator is here! Kalman Filter is a quantitative tool created by Rudolf E. Kalman. In the case of trading, it can help smooth out the price data that traders observe, making it easier to identify underlying trends. The Kalman Filter is particularly useful for handling price data that is noisy and unpredictable. As an adaptive-based algorithm, it can easily adjust to new data, which makes it a handy tool for traders operating in markets that are prone to change quickly.
Many people may assume that the Kalman Filter is the same as a Moving Average, but that is not the case. While both tools aim to smooth data and find trends, they serve different purposes and have their own sets of advantages and disadvantages. The Kalman Filter provides a more dynamic and adaptive approach, making it suitable for real-time analysis and predictive capabilities, but it is also more complex. On the other hand, Moving Averages offer a simpler and more intuitive way to visualize trends, which makes them a popular choice among traders for technical analysis. However, the Moving Average is a lagging indicator and less adaptive to market change, if it's adjusted it may result in overfitting. In this case, the Kalman Filter would be a better choice for smoothing the price up.
I hope you find this indicator useful! It's been an exciting and extensive journey since I began diving into the world of finance and trading. I'll keep you all updated on any new indicators I discover that could benefit the community in the future. Until then, take care, and happy trading! Ciao.
SFC Valuation Model - Fair ValueValuation is the analytical process of determining the current (or projected) worth of an asset or a company. There are many techniques used for doing a valuation. An analyst placing a value on a company looks at the business's management, the composition of its capital structure, the prospect of future earnings, and the market value of its assets, among other metrics.
Fundamental analysis is often employed in valuation, although several other methods may be employed such as the capital asset pricing model (CAPM) or the dividend discount model (DDM), Discounted Cash Flow (DCF) and many others.
A valuation can be useful when trying to determine the fair value of a security, which is determined by what a buyer is willing to pay a seller, assuming both parties enter the transaction willingly. When a security trades on an exchange, buyers and sellers determine the market value of a stock or bond.
There is no universal standard for calculating the intrinsic value of a company or stock. Financial analysts attempt to determine an asset's intrinsic value by using fundamental and technical analyses to gauge its actual financial performance.
Intrinsic value is useful because it can help an investor understand whether a potential investment is overvalued or undervalued.
This indicator allows investors to simulate different scenarios depending on their view of the stock's value. It calculates different models automatically, but users can define the fair value manually by changing the settings.
For example: change the weight of the model; choose how conservatively want to evaluate the stock; use different growth rate or discount rate and so on.
The indicator shows other useful metrics in order to help investors to evaluate the stock.
This indicator can save users hours of searching financial data and calculating fair value.
There are few valuation methods/steps
- Macroeconomics - analyse the current economic;
- Define how the sector is performing;
- Relative valuation method - compare few stocks and find the Outlier;
- Absolute valuation method historically- define how the stock performed in the past;
- Absolute valuation method - define how the stock is performed now and find the fair value;
- Technical analysis
How to use:
1. Once you have completed the initial evaluation steps, simply load the indicator.
2. Check the default settings and see if they suit you.
3. Find the fair value and wait for the stock to reach it.
Returns Model by TenozenHey there! I've been diving into the book "Paul Wilmott on Quantitative Finance," and I stumbled upon this cool model for calculating and modeling returns. Basically, it helps us figure out how much a price has changed over a set number of periods—I like to use 20 periods as a default. Once we get that rate of change value, we crunch some numbers to find the standard deviation and mean using all the historical data we have. That's the foundation of this model.
Now, let's talk about how to use it. This model shows us how returns and price behavior are connected. When returns hang out in the +1 to +2 standard deviation range, it usually means returns are about to drop, and vice versa. Often, this leads to corresponding price moves. But here's the thing: sometimes prices don't do what we expect. Why? It's because there's another hidden factor at play—I like to call it "power."
This "power" isn't something we can see directly, but it's there. Basically, when returns are within that standard deviation range, the market faces resistance when trying to move in its preferred direction, whether bullish or bearish. The strength of this "power" determines if the market will snap back to the average or go for a wild ride. It can show up as small price wiggles, big price jumps, or lightning-fast moves. By understanding this "power," we can get a better handle on what the market might do next and avoid getting blindsided. In the meantime, I couldn't explain "power" yet, but In the future, when I've learned enough, I'd love to share the model with you guys!
So... I'm planning to explore and share more models from this book as I learn, even if those pesky math formulas can be tough to crack. I hope you find this indicator as helpful as I do, and if you've got any suggestions or feedback, please feel free to share! Ciao!
Ratio To Average - The Quant ScienceRatio To Average - The Quant Science is a quantitative indicator that calculates the percentage ratio of the market price in relation to a reference average. The indicator allows the calculation of the ratio using four different types of averages: SMA, EMA, WMA, and HMA. The ratio is represented by a series of histograms that highlight periods when the ratio is positive (in green) and periods when the ratio is negative (in red).
What is the Ratio to Average?
The Ratio to Average is a measure that tracks the price movements with one of its averages, calculating how much the price is above or below its own average, in percentage terms.
USER INTERFACE
Lenght: it adjusts the number of bars to include in the calculation of the average.
Moving Average: it allows you to choose the type of average to use.
Color Up/Color Down : it allows you to choose the color of the indicator for positive and negative ratios.
Autoregressive Covariance Oscillator by TenozenWell to be honest I don't know what to name this indicator lol. But anyway, here is my another original work! Gonna give some background of why I create this indicator, it's all pretty much a coincidence when I'm learning about time series analysis.
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Well, the formula of Auto-covariance is:
E{(X(t)-(t) * (X(t-s)-(t-s))}= Y_s
But I don't multiply both values but rather subtract them:
E{(X(t)-(t) - (X(t-s)-(t-s))}= Y_s?
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For arm_vald, the equation is as follows:
arm_vald = val_mu + mu_plus_lsm + et
val_mu --> mean of time series
mu_plus_lsm --> val_mu + LSM
et --> error term
As you can see, val_mu^2. I did this so the oscillator is much smoother.
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After I get the value, I normalize them:
aco = Y_s? / arm_vald
So by this calculation, I get something like an oscillator!
(more details in the code)
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So how to use this indicator? It's so easy! If the value is above 0, we gonna expect a bullish response, if the value is below 0, we gonna expect a bearish response; that simple. Be aware that you should wait for the price to be closed before executing a trade.
Well, try it out! So far this is the most powerful indicator that I've created, hope it's useful. Ciao.
(more updates for the indicator if needed)
Premium Linear Regression - The Quant ScienceThis script calculates the average deviation of the source data from the linear regression. When used with the indicator, it can plot the data line and display various pieces of information, including the maximum average dispersion around the linear regression.
The code includes various user configurations, allowing for the specification of the start and end dates of the period for which to calculate linear regression, the length of the period to use for the calculation, and the data source to use.
The indicator is designed for multi-timeframe use and to facilitate analysis for traders who use regression models in their analysis. It displays a green linear regression line when the price is above the line and a red line when the price is below. The indicator also highlights areas of dispersion around the regression using circles, with bullish areas shown in green and bearish areas shown in red.
Quantitative Price Forecasting - The Quant ScienceThis script is a quantitative price forecasting indicator that forecasts price changes for a given asset.
The model aims to forecast future prices by analyzing past data within a selected time period. Mathematical probability is used to calculate whether starting from time X can lead to reaching prices Y1 and Y2. In this context, X represents the current selected time period, Y1 represents the selected percentage decrease, and Y2 represents the selected percentage increase. The probabilities are estimated using the simple average.
The simple average is displayed on the chart, showing in red the periods where the price is below the average and in green the periods where the price is above the average.
This powerful tool not only provides forecasts of future prices but also calculates the distribution of variations around the average. It then takes this information and creates an estimate of the average price variation around the simple average.
Using a mean-reverting logic, buying and selling opportunities are highlighted.
We recommend turning off the display of bars on your chart for a better experience when using this indicator.
Unlock the full potential of your trading strategy with our powerful indicator. By analyzing past price data, it provides accurate forecasts and calculates the probability of reaching specific price targets. Its mean-reverting logic highlights buying and selling opportunities, while the simple moving average displayed on the chart shows periods where the price is above or below the average. Additionally, it estimates the average variation of price around the simple average, giving you valuable insights into price movements. Don't miss out on this valuable tool that can take your trading to the next level
Probabilities Module - The Quant Science This module can be integrate in your code strategy or indicator and will help you to calculate the percentage probability on specific event inside your strategy. The main goal is improve and simplify the workflow if you are trying to build a quantitative strategy or indicator based on statistics or reinforcement model.
Logic
The script made a simulation inside your code based on a single event. For single event mean a trading logic composed by three different objects: entry, take profit, stop loss.
The script scrape in the past through a look back function and return the positive percentage probability about the positive event inside the data sample. In this way you are able to understand and calculate how many time (in percentage term) the conditions inside the single event are positive, helping to create your statistical edge.
You can adjust the look back period in you user interface.
How can set up the module for your use case
At the top of the script you can find:
1. entry_condition : replace the default condition with your specific entry condition.
2. TPcondition_exit : replace the default condition with your specific take profit condition.
3. SLcondition_exit : replace the default condition with your specific stop loss condition.
Yield Trend Indicator - The Quant ScienceYield Trend Indicator - The Quant Science™ is a quantitative indicator representing percentage yields and average percentage yields of three different assets.
Percentage yields are fundamental data for all quantitative analysts. This indicator was created to offer immediate calculations and represent them through an indicator consisting of lines and columns. The columns represent the percentage yield of the current timeframe, for each asset. The lines represent the average percentage yield, of the current timeframe, for each asset.
The user easily adds tickers from the user interface and the algorithm will automatically create the quantitative data of the chosen assets.
The blue refers to the main asset, the main set on the chart.
The yellow refers to the second asset, added by the user interface.
The red refers to the third asset, added by the user interface.
The timeframe is for all assets the one set to the chart, if you use a chart with timeframe D, all data is processed on this timeframe. You can use this indicator on all timeframes without any restrictions.
The user can change the type of formula for calculating the average yield easily via the user interface. This software includes the following formulas:
1. SMA (Simple Moving Average)
2. EMA (Exponential Moving Average)
3. WMA (Weighted Moving Average)
4. VWMA (Volume Weighted Moving Average)
The user can customize the indicator easily through the user interface, changing colours and many other parameters to represent the data on the chart.
Ethereum OnChain Data Indicator - The Quant ScienceEthereum On Chain Data Indicator - The Quant Science™ is a quantitative indicator created for mid-long term analysis.
The indicator uses quantitative statistics to recreate a model that represents the most important data from the on-chain analysis for the Ethereum blockchain.
The on-chain data used to create this model are:
1. Total weekly transactions
2. Total monthly transactions
3. Frequency of transactions per second on a daily scale
4. Frequency of transactions per second on a weekly scale
5. Amount of Ethereum burned on a daily scale
6. Amount of Ethereum burned on a weekly scale
7. Volume of short positions on a daily scale
8. Volume of short positions on a weekly scale
9. Volume of short positions more/less than average on a daily scale
10. Volume of short positions more/less than average on a weekly scale
All these data were extrapolated and manipulated using the mean and standard deviation.
The end result is a powerful tool that enables mid-long term investors and traders to analyze on-chain data through quantitative analysis.
FEATURES
The blue color area refers to the average change in data on a weekly scale. The light blue colored area indicates the monthly changes in the data. It is interesting to observe the correlation relationship between price and times when short-run data increases compared to long-run data and vice versa.
The more intense purple histograms refer to the standard deviation of the mean change in data on an annual scale. Histograms of less intense purple color refer to the standard deviation of the mean variation of data on a monthly scale. It is interesting to observe the ratio of the standard deviation between two different time periods.
This indicator can be used to perform statistical comparative analysis for manual and mid-long term investments. It can also be used to create auto trading strategies when used and integrated within an algorithm.
On-chain data are updated every 24 hours, so the timeframes to be used for analysis with this indicator are: D, 4H, 1H.
Volume Variation Index IndicatorThis tool is a quantitative tip for analysts who study volumes or create volume based trading strategies.
Like all our projects, we start with a statistical logic to which we add coding logic.
This indicator can save a huge amount of time in calculating the variation of volume between sessions .
How it work
The indicator calculates the difference between the volume of the last closing bar and the volume of the previous closing bar. It shows the difference between the trading volumes.
The session in which the trading volume is up are represented in green.
Red session represent trading volume down.
We have added a third function.
Through the User Interface the trader can activate or deactivate the variation average.
The indicator is able to calculate the average of the volume changes by representing it with a blue line.
To activate the average, simply set it to ON in the User Interface.
By default, the indicator calculates the average of the last 10 periods, but you are free to set this parameter in the User Interface.
Data access
To access the data, simply move the cursor. When you move the cursor over the green bars, the increase data will be displayed in green. By hovering the cursor over the red bars you will see the decrease data in red. By hovering the cursor over the average will show you the average data in blue.
The data is displayed in the top left corner of the indicator dashboard.
If you found this indicator helpful, please like our script.
Prime Distance Frame Quant Model for Risk Reward & Pivot PointsIn this script we take all of the prime numbers up to 100 and plot them as olive lines and then consider the distance between two adjacent plots and color code these distances with the fill function. This allows us to find higher and lower prime gaps allowing us to make much more informed decisions on our risk reward for a given trade and the levels where we should consider taking profit.
The Script includes scaling for all assets and is intended to be used for crypto trading.
ALMA PhysicsA super simple yet elegant indicator, "ALMA Physics" calculates the derivatives of the Arnaud Legoux Moving Average (ALMA) with respect to Time. Both the ALMA parameters and the time variable can be modified in the indicator's settings.
derivatives "physics":
Blue - ALMA Velocity (dALMA / dt)
Magenta - ALMA Acceleration (d_Velocity / dt)
White - ALMA Jerk (d_Acceleration / dt)
The indicator may be used to gauge market momentum. The simple code can also be used for pedagogical purposes for anyone interested in developing indicators in TradingView.
💸 Divergence Finder v1.0 by Cryptothythms💸 Divergence Finder v1.0 by Cryptothythms
Intro
This indicator is meant as a quantitative look at divergences. Special thanks to RicardoSantos (legend!) for his divergence detection script which was used as a basis to construct this.
Usage
Lime Green = Regular Bullish Divergence
Dark Green = Hidden Bullish Divergence
Red = Regular Bearish Divergence
Maroon = Hidden Bearish Divergence
Remember... divergences do not have a high probability success overall. So to mitigate that this is an attempt to source many "opinions" on the state of divergence from many different classes of indicators.
Leading indicators - Lead price action, but have a higher probability of false signals
Real time indicators - Track price action, momentum, etc in a tick by tick state. Generally a 1-3 bars lag can be present.
Lagging indicators - Lag price action a little, but offer more in terms of confirmation of the divergence
Volume indicators - Look at volume flow in different fashions and give an added dimension to divergence finding. Remember volume divergences interact with price independently. For instance volume can be going down, but price going up. Be sure to understand the relationship of volume and price action when considering these readings.
👍 Enjoying this indicator or find it useful? Please give me a like and follow! I post crypto analysis, price action strategies and free indicators regularly.
💬 Questions? Comments? Want to get access to an entire suite of proven trading indicators? Come visit us on telegram and chat, or just soak up some knowledge. We make timely posts about the market, news, and strategy everyday. Our community isn't open only to subscribers - everyone is welcome to join.
For Trialers & Chat: t.me/cryptorhythms
Mathematical Derivatives of PriceThis indicator is meant to show the Velocity (1st order derivative), Acceleration (2nd order derivative), Jerk (3rd order derivative), Snap (4th order derivative), Crackle (5th order derivative), & Pop (6th order derivative) of price. The values at the top of the indicator window are in this order from left to right. I don't particularly know how this would be used in a trading strategy, but if you're ever curious about how quickly price is moving and how much it is accelerating, then you could use this tool.
*If you only care about velocity and acceleration, and don't like how squished the window is because of the long decimal numbers then edit the "precision" value in the first line of the script to a smaller number of your choosing.*
DVDIQQE [DW]This is an experimental study inspired by the Quantitative Qualitative Estimation indicator designed to identify trend and wave activity.
In this study, rather than using RSI for the calculation, the Dual Volume Divergence Index oscillator is utilized.
First, the DVDI oscillator is calculated by taking the difference between PVI and its EMA, and NVI and its EMA, then taking the difference between the two results.
Optional parameters for DVDI calculation are included within this script:
- An option to use tick volume rather than real volume for the volume source
- An option to use cumulative data, which sums the movements of the oscillator from the beginning to the end of TradingView's maximum window to give a more broad picture of market sentiment
Next, two trailing levels are calculated using the average true range of the oscillator. The levels are then used to determine wave direction.
Lastly, rather than using 0 as the center line, it is instead calculated by taking a cumulative average of the oscillator.
Custom bar colors are included.
Note: For charts that have no real volume component, use tick volume as the volume source.
Quantitative Qualitative Estimation (QQE)The indicator QQE, is an interesting tool based on a Relative Strength Index (RSI). While the original RSI is often used as a pointer for overbought or oversold market phases, the QQE provides additional information. Use the QQE to display trend direction and trend strength .
For me this is one of the most important indicator for Trend Following.
##This QQE indicator is an improved version made by 'mladen' for Metatrader 4.
The histogram does not differ from the original QQE! The developer has adapted the scaling so that the central horizontal level is zero. It has no effect to the result, but is much more convenient to analyze the trend.
Main Signals
Background changes when the black line crosses the grey line.
Identify the trend direction
Singal turns green while the main QQE trendline is above the zero line and red while it is below.
This works best in the major timeframes like Daily or Weekly.
You can activate this signal in the settings.
NYSE:THO
Identify the trend strength
_Histogram Colors_
Green (above 10): bullish
Red (below -10): bearish
Yellow: flat
It is not a buy or sell signal when the color of the histogram changes. It only says that one side could gained the advantage.
If you use a large timeframe like Monthly, you can reduce the number of false signal by setting the SF (Slow Factor) from 5 (default) to 1.
S&P 500, Monthly
Please always remember, there is no holy grail indicator!
...but this one defines trends quite accurately.
